Seroprevalence of Immunoglobulin G against SARS-CoV-2 in Iran at National and Sub- National Levels: Population-based Sero-Epidemiological Data from STEPS Survey 2021

doi:10.21203/rs.3.rs-3143444/v1

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Seroprevalence of Immunoglobulin G against SARS-CoV-2 in Iran at National and Sub- National Levels: Population-based Sero-Epidemiological Data from STEPS Survey 2021

https://doi.org/10.21203/rs.3.rs-3143444/v1

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PCR tests for COVID-19 have relatively low sensitivity and only symptomatic cases have been tested. Thus, a population-based seroprevalence study was necessary to determine the extent of missed cases. The objective of this study was to achieve a realistic infection rate in Iran and probe into some explanations behind being infected or not. In this population-based cross-sectional study, 27,874 adults aged more than 18 from 31 provinces from February to April 2021 were included. The seroprevalence of IgG against SARS-CoV-2 in Iran was 20.63% (19.71-21.56) and 16.25% (15.11-17.41) based on two different adjustments. The age-standardized seroprevalence was relatively high among Kurdistan [30.29% (26.04-34.55) and 28.31% (23-33.61)], and West Azarbayejan [29.33% (24.85-33.8) and 27.11% (21.52-32.68)]. Smoking, higher education, being underweight, male, and single were protective factors, and higher daily interactions was a risk for infection. It is evident that reported rates have been misleading. Furthermore, several intervenable factors can predict the risk of infection.

Biological sciences/Immunology

Health sciences/Diseases

Health sciences/Medical research

Health sciences/Risk factors

COVID-19

Seroprevalence

IgG

STEPS Survey

Non-communicable disease risk factors

smoking

Physical activity

COVID-19 was first reported in the Wuhan province of China and has been spreading across the globe ever since (1). Without determining the number of infected people, it is impossible to implement cost-effective measures to halt the virus spread. Nevertheless, determining the actual infection rates has remained challenging for health systems due to the high proportion of asymptomatic infected individuals (2, 3).

COVID-19 diagnosis is primarily based on reverse-transcription polymerase chain reaction (RT–PCR), where specimen collection, transportation, and storage methods might affect the test’s sensitivity (4, 5). However, the use of PCR testing for population-based studies is challenged by its relatively low sensitivity (9), along with some countries’ preferred approaches to only test symptomatic or suspected cases due to limited resources (6).

The anti-SARS-CoV-2 antibodies in serum samples would be positive even if the individual had been asymptomatic (7). Although both anti-SARS-CoV-2 immunoglobulin G (IgG) and M (IgM) could be measured to determine the infection history of an individual, IgG tends to remain higher for more extended periods (8). The IgG is more likely to be detected in participants’ serum samples in a cross-sectional study. Hence, population-based seroprevalence studies are required to achieve the proportion of infected and not infected people and probe into some possible explanations behind being infected or not. Such results would also be valuable for investigating the effectiveness of measures taken by governments and people in mitigating the infection rate for future waves of pandemics before the availability of vaccines.

Indeed, a population-based sero-epidemilogical data would shed light on the extent of past infections and immune responses, aiding in the formulation of evidence-based strategies for public health interventions. Hence, the objective of this study was to determine the seroprevalence of IgG against SARS-CoV-2 and its sociodemographic, medical, and behavioral determinants in Iran.

2.1 Overview

In this population-based cross-sectional study, the data were extracted from the STEP-wise approach to non-communicable disease risk factor surveillance (STEPS) 2021 survey, which was conducted with the participation of 27,874 adults aged more than 18 from 31 provinces of Iran. This study was approved by the Ethical Committee of the National Institute for Health Research and it was conducted in accordance with relevant guidelines/regulations. Besides, in this study informed consent was obtained from all participants or their legal guardians. There were 16,610 individual-level valid serological sample results that were considered IgG positive or negative and were 25 and above. After that, the calculated age-standardized seroprevalences at national and sub-national levels were adjusted by two adjustments of sensitivity and specificity. Finally, the Odds Ratio (OR) of sociodemographic variables, behaviors, past medical history, and compliance to COVID-19 safety protocols with the IgG seropositivity were calculated.

2.2 Data source

STEPS survey, initially introduced by the World Health Organization, is a standardized and straightforward method for collecting and analyzing data on NCDs’ risk factors across a country. It can include a wide range of topics (9) and have been conducted in Iran in 2005–2009, 2011, and 2016 (10). In the 2021 survey, the data was gathered from February 2021 to April 2021 (Table S1). Following some changes in the original project introduced by the World Health Organization, due to COVID-19 pandemic, the anti-SARS-CoV-2 IgG ELISA tests have been run on serum samples collected from participants, and COVID-19 related variables, including compliance with preventive measures, were studied.

2.3 Variables

The variables included in this study included IgG test results, sociodemographic variables including age, sex, education, occupation, marital status, quality of life, and wealth index; past medical history including diabetes mellitus, hypertension, hypercholesterolemia, myocardial infarction, lung disease, stroke, cancer, and kidney disease; behavioral variables including current cigarette smoking, second-hand smoking, physical activity (i.e. Metabolic Equivalent of Task [MET]), and alcohol consumption; BMI; COVID-19 safety protocols compliance including wearing masks, face shields, gloves, and washing hands. A detailed description of quality of life, wealth index, MET, and alcohol consumption is in the supplementary material, page 2.

2.4 Serum IgG measurement and seroprevalence adjustment

IgG assessments were conducted via of SARS-CoV-2 ELISA diagnostic kit provided by Pishtaz Teb Diagnostics (11) (supplementary material, page 2), approved by Iran’s Food and Drug Administration. After determining the optical density of each sample and determining the cut-off value, samples were categorized as IgG positive or negative. To calculate the adjusted prevalence of seropositive individuals, the test’s sensitivity and specificity were inserted into the below formula (12):

$$Adjusted seroprevalence=\frac{crude seroprevalence-(1-specificity)}{sensitivity-(1-specificity)}$$

The provider-reported (adjustment 1) sensitivity and specificity of these kits were 94.1% and 98.3%, respectively. However, the Iran’s Pasteur Institute further evaluated the kits’ specs to validate them (adjustment 2). The secondary reported sensitivity and specificity were 82.8% and 91.28%, respectively. In this study, both adjustments were included.

2.5 Statistical Analysis

The ORs were calculated between all of the abovementioned variables and IgG seropositivity and the P values were calculated. The crude ORs were calculated in addition to the adjusted ORs by age group, sex, education and wealth index. All of the results are reported with 95% Confidence Interval (CI). The statistical analyses and visualizations were carried out using R statistical packages v4.1.2 (http://www.r-project.org, RRID: SCR_001905) (Figure S1).

3.1 SARS-CoV-2 seroprevalence

The seroprevalence of IgG against SARS-CoV-2 in Iran at the national level was 20.63% (19.71–21.56) and 16.25% (15.11–17.41) based on first and second adjustment, respectively (Table 1). At the province level, there were disparities in the seroprevalence of IgG. The highest seroprevalence was in Kurdistan with 30.29% (26.04–34.55) by the first and 28.31% (23-33.61) by the second adjustment, and the lowest seroprevalence was in Hormozgan with 10.75% (6.76–14.73) by the first and 3.93% (0-8.9) by the second adjustment (Table 1 and Fig. 1).

Table 1

Age-standardized seroprevalence of IgG against SARS-CoV-2 in Iran at national and subnational level by first and second adjustments
Location	Sample size	N	Crude prevalence % (95% UI))	Prevalence by adjustment 1% (95% UI)	Prevalence by adjustment 2% (95% UI)
National	16610	3598	20.76% (19.91–21.62)	20.63% (19.71–21.56)	16.25% (15.11–17.41)
Alborz	237	36	15.36% (9.07–21.66)	14.78% (7.98–21.6)	8.96% (0.47–17.47)
Ardebil	423	112	22.21% (17.84–26.59)	22.2% (17.47–26.94)	18.21% (12.31–24.12)
Azarbayejan-East	483	108	18.24% (14.37–22.12)	17.9% (13.71–22.1)	12.85% (7.63–18.09)
Azarbayejan-West	538	169	28.8% (24.66–32.93)	29.33% (24.85–33.8)	27.11% (21.52–32.68)
Bushehr	515	64	11.92% (8.88–14.97)	11.06% (7.77–14.36)	4.32% (0.22–8.44)
Chahar Mahaal and Bakhtiari	478	108	21.65% (17.52–25.79)	21.59% (17.12–26.07)	17.45% (11.88–23.04)
Fars	870	196	21.68% (18.52–24.84)	21.62% (18.2-25.04)	17.49% (13.23–21.76)
Gilan	372	59	13.62% (9.62–17.62)	12.9% (8.57–17.23)	6.61% (1.21–12.01)
Golestan	524	118	21.1% (17.45–24.76)	21% (17.05–24.96)	16.71% (11.78–21.65)
Hamedan	570	138	22.98% (19.01–26.94)	23.03% (18.73–27.32)	19.25% (13.89–24.6)
Hormozgan	365	38	11.63% (7.95–15.31)	10.75% (6.76–14.73)	3.93% (0-8.9)
Ilam	360	103	27.94% (22.88–32.99)	28.4% (22.92–33.86)	25.94% (19.11–32.76)
Isfahan	581	125	18.36% (14.82–21.91)	18.03% (14.2-21.87)	13.01% (8.23–17.81)
Kerman	376	60	18.43% (13.37–23.49)	18.11% (12.63–23.58)	13.11% (6.28–19.94)
Kermanshah	404	91	20.69% (15.78–25.6)	20.55% (15.24–25.87)	16.16% (9.53–22.79)
Khorasan-North	578	105	18.14% (14.75–21.53)	17.79% (14.12–21.46)	12.72% (8.14–17.29)
Khorasan-Razavi	1001	246	22.56% (19.71–25.4)	22.58% (19.49–25.65)	18.68% (14.84–22.52)
Khorasan-South	510	121	22.16% (18.08–26.23)	22.14% (17.73–26.55)	18.14% (12.63–23.64)
Khuzestan	597	84	12.4% (9.6-15.19)	11.58% (8.55–14.6)	4.97% (1.19–8.73)
Kohgiluyeh and Boyer-Ahmad	492	82	15.76% (12.4-19.13)	15.22% (11.58–18.86)	9.5% (4.97–14.05)
Kurdistan	579	181	29.69% (25.76–33.62)	30.29% (26.04–34.55)	28.31% (23-33.61)
Lorestan	604	136	21.73% (18.13–25.34)	21.68% (17.78–25.58)	17.56% (12.7-22.44)
Markazi	485	124	23.23% (18.68–27.77)	23.3% (18.38–28.21)	19.59% (13.44–25.72)
Mazandaran	514	91	17.08% (13.13–21.04)	16.65% (12.37–20.93)	11.29% (5.95–16.63)
Qazvin	415	86	19.36% (15.11–23.62)	19.11% (14.51–23.72)	14.36% (8.63–20.11)
Qom	413	96	24.8% (19.06–30.54)	25% (18.79–31.21)	21.71% (13.96–29.45)
Semnan	451	99	22.13% (17.17–27.1)	22.11% (16.74–27.49)	18.1% (11.41–24.81)
Sistan and Baluchistan	609	99	15.56% (12.29–18.82)	15% (11.46–18.53)	9.23% (4.82–13.63)
Tehran	1099	237	17.23% (14.68–19.78)	16.81% (14.05–19.57)	11.49% (8.05–14.93)
Yazd	387	91	22.07% (16.54–27.6)	22.05% (16.06–28.03)	18.02% (10.56–25.49)
Zanjan	780	195	24.48% (21.11–27.85)	24.65% (21.01–28.3)	21.27% (16.73–25.82)

3.2 IgG seroprevalence association with sociodemographic factors

In the country, the individuals over 60 years old had higher IgG seroprevalence with 22.93% (20.28–25.59) by the second adjustment (OR: 1.599 [1.251–2.045], P < 0.001). Age distribution of IgG seropositive individuals at the subnational level is demonstrated in Fig. 2.

Moreover, men had lower IgG seroprevalence than women 14.28% (12.59–15.97) Vs 17.85% (16.28–19.41) by the second adjustment (OR: 0.867 [0.775–0.969], P = 0.012). Sex distribution of IgG seropositive individuals at the subnational level is demonstrated in Fig. 3. Participants with higher years of education had lower IgG seroprevalence as well. The IgG seroprevalence among the participants with over 12 years of education was 11.83% (9.89–13.74) by the second adjustment (OR: 0.651 [0.533–0.793], P < 0.001). Furthermore, participants who were single had lower IgG seroprevalence, which was 6.47% (3.27–9.67) by the second adjustment (OR: 0.747 [0.593–0.942], P = 0.014). The upper-middle wealth index, on the other hand, was associated with higher IgG seroprevalence with 19.02% (16.32–21.71) by the second adjustment (OR: 1.534 [1.299–1.812], P < 0.001) (Tables 2 and 3, Fig. 4).

Table 2

The crude and adjusted seroprevalences IgG among the different statuses of study variables
Variable	Status	N	Frequency % (95% UI)	Crude prevalence (95% UI)	Prevalence by adjustment 1 (95% UI)	Prevalence by adjustment 2 (95% UI)
Sociodemographic
Age
	25–29	1390	8.57% (7.95–9.18)	15.07% (12.72–17.42)	14.47% (11.93–17.01)	8.57% (5.4–11.74)
	30–39	3978	24.56% (23.6–25.53)	16.29% (14.74–17.84)	15.79% (14.11–17.47)	10.22% (8.13–12.31)
	40–49	3968	23.19% (22.29–24.09)	20.05% (18.31–21.8)	19.86% (17.98–21.75)	15.29% (12.95–17.66)
	50–59	3405	20.07% (19.22–20.93)	23.67% (21.73–25.62)	23.78% (21.68–25.89)	20.18% (17.56–22.81)
	60 and above	3869	23.61% (22.66–24.56)	25.71% (23.74–27.68)	25.98% (23.85–28.12)	22.93% (20.28–25.59)
Sex
	Female	9424	55.49% (54.39–56.58)	21.94% (20.78–23.1)	21.9% (20.65–23.16)	17.85% (16.28–19.41)
	Male	7186	44.51% (43.42–45.61)	19.3% (18.05–20.55)	19.05% (17.69–20.4)	14.28% (12.59–15.97)
Years of education
	0 y	2929	14.63% (13.97–15.28)	27.73% (25.57–29.88)	28.17% (25.83–30.5)	25.66% (22.75–28.56)
	1 to 7 y	4615	25.4% (24.5–26.29)	22.71% (21.16–24.26)	22.74% (21.06–24.42)	18.88% (16.79–20.98)
	7 to 12 y	3054	19.15% (18.31–19.99)	19.65% (17.8–21.5)	19.43% (17.42–21.43)	14.75% (12.26–17.25)
	12 + y	5896	40.83% (39.72–41.93)	17.48% (16.05–18.9)	17.08% (15.53–18.61)	11.83% (9.89–13.74)
Occupation
	Freelancer	3846	24.31% (23.37–25.26)	18.83% (17.2–20.46)	18.54% (16.77–20.3)	13.65% (11.45–15.85)
	Private sector worker	972	6.65% (6.09–7.21)	15.82% (12.63–19.02)	15.28% (11.83–18.74)	9.58% (5.28–13.9)
	Public sector worker	1176	7.41% (6.75–8.06)	17.75% (14.73–20.76)	17.37% (14.1–20.63)	12.19% (8.11–16.25)
	Retired	1584	10.43% (9.72–11.15)	22.98% (19.86–26.09)	23.03% (19.65–26.4)	19.25% (15.04–23.45)
	Unemployed	818	4.51% (4.1–4.91)	17.53% (14.29–20.77)	17.13% (13.63–20.64)	11.89% (7.52–16.27)
	Unpaid job	8098	46.69% (45.6–47.78)	22.68% (21.43–23.93)	22.71% (21.35–24.06)	18.84% (17.16–20.53)
Marital status
	Single	1312	8.45% (7.81–9.09)	13.51% (11.14–15.88)	12.78% (10.22–15.35)	6.47% (3.27–9.67)
	Married	13825	82.46% (81.61–83.32)	21.18% (20.23–22.13)	21.08% (20.05–22.11)	16.82% (15.54–18.1)
	Divorced/separate with partner	362	2.43% (2.06–2.79)	16.68% (12.27–21.1)	16.21% (11.44–21)	10.75% (4.79–16.71)
	Widow	1111	6.66% (6.13–7.18)	26.35% (22.8–29.9)	26.68% (22.84–30.52)	23.8% (19.01–28.59)
Quality of life
	1	3850	21.93% (21.06–22.8)	22.55% (20.75–24.36)	22.56% (20.62–24.52)	18.67% (16.24–21.11)
	2	4031	24.36% (23.39–25.32)	21.31% (19.53–23.1)	21.22% (19.3–23.16)	17% (14.59–19.41)
	3	3753	23.18% (22.26–24.1)	20.97% (19.19–22.75)	20.85% (18.93–22.78)	16.54% (14.13–18.94)
	4	2543	17.1% (16.21–17.99)	18.62% (16.58–20.66)	18.31% (16.1–20.52)	13.36% (10.61–16.12)
	5	2345	13.44% (12.74–14.13)	18.67% (16.63–20.7)	18.37% (16.16–20.56)	13.43% (10.68–16.17)
Wealth index
	Poorest	3419	19.57% (18.7–20.44)	18.62% (16.88–20.36)	18.31% (16.43–20.19)	13.36% (11.02–15.71)
	Lower middle	3049	20.06% (19.14–20.97)	22.18% (20.18–24.18)	22.16% (20–24.33)	18.17% (15.47–20.87)
	Middle	3395	19.01% (18.22–19.79)	21.97% (20.22–23.73)	21.94% (20.04–23.84)	17.89% (15.52–20.26)
	Upper middle	3167	20% (19.15–20.85)	22.81% (20.81–24.8)	22.85% (20.68–25)	19.02% (16.32–21.71)
	Richest	2725	21.37% (20.33–22.41)	18.56% (16.36–20.77)	18.25% (15.87–20.64)	13.28% (10.31–16.27)
Comorbidities
Diabetes Mellitus		2251	14.22% (13.46–14.99)	25.06% (22.67–27.44)	25.28% (22.69–27.86)	22.06% (18.83–25.27)
Hypertension		6102	35.68% (34.62–36.73)	24.67% (23.14–26.2)	24.86% (23.2–26.52)	21.53% (19.47–23.6)
Hypercholesterolemia		5096	30.61% (29.59–31.62)	24.47% (22.81–26.13)	24.64% (22.85–26.44)	21.26% (19.02–23.5)
MI (ever)		1237	7.7% (7.11–8.28)	25.11% (21.79–28.43)	25.34% (21.74–28.93)	22.12% (17.64–26.61)
MI (last year)		325	2.16% (1.8–2.51)	26.51% (19.56–33.46)	26.85% (19.33–34.37)	24.01% (14.63–33.4)
Lung disease (ever)		836	4.87% (4.43–5.31)	24.3% (20.6–28)	24.46% (20.45–28.46)	21.03% (16.04–26.03)
Lung disease (last year)		473	2.84% (2.49–3.19)	24.62% (19.61–29.64)	24.81% (19.38–30.24)	21.46% (14.7–28.24)
Stroke (ever)		249	1.43% (1.19–1.67)	27.6% (20.27–34.93)	28.03% (20.1–35.96)	25.49% (15.59–35.38)
Stroke (last year)		43	0.22% (0.13–0.31)	23.87% (9.59–38.16)	23.99% (8.54–39.46)	20.45% (1.17–39.74)
Cancer (last year)		217	1.36% (1.12–1.6)	17.26% (10.65–23.88)	16.84% (9.69–24)	11.53% (2.61–20.46)
Kidney disease		2019	12.05% (11.31–12.79)	25.85% (23.19–28.52)	26.14% (23.26–29.03)	23.12% (19.53–26.73)
Behavioral factors
Current cigarette smoker	Yes	1492	9.51% (8.83–10.18)	12.57% (10.06–15.08)	11.76% (9.05–14.48)	5.2% (1.81–8.59)
Second-hand smoker	Yes	4639	30.6% (29.57–31.64)	21.91% (20.24–23.58)	21.87% (20.06–23.68)	17.81% (15.55–20.06)
Physical activity (MET)	Yes	7435	51.01% (49.85–52.17)	21.09% (19.85–22.34)	20.98% (19.64–22.34)	16.7% (15.02–18.39)
Tooth brush	Yes	13095	82.15% (81.39–82.91)	20.25% (19.29–21.21)	20.08% (19.04–21.11)	15.56% (14.27–16.86)
Dental floss	Yes	3107	21.87% (20.89–22.85)	19.81% (17.74–21.88)	19.6% (17.36–21.84)	14.97% (12.18–17.76)
No oral hygiene	Yes	2946	14.84% (14.13–15.54)	23.08% (21.11–25.05)	23.14% (21.01–25.27)	19.38% (16.73–22.04)
Alcohol consumption frequency
	Never	15969	96.56% (96.11–97.01)	20.88% (20.01–21.75)	20.76% (19.82–21.7)	16.41% (15.24–17.59)
	Daily	14	0.06% (0.02–0.1)	5.37% (0- 15.95)	3.97% (0–15.42)	-4.52% (-18.79–9.76)
	Weekly	75	0.51% (0.38–0.64)	14.24% (6.13–22.35)	13.57% (4.79–22.35)	7.45% (0–18.4)
	Monthly	149	1.23% (0.92–1.55)	17.48% (7.63–27.32)	17.08% (6.42–27.73)	11.83% (0–25.11)
	Seasonal	104	0.71% (0.47–0.95)	12.92% (5.21–20.62)	12.14% (3.8–20.48)	5.67% (0–16.06)
	6 months or more	153	0.93% (0.75–1.1)	24.39% (16–32.78)	24.56% (15.48–33.64)	21.15% (9.83–32.48)
BMI
	Underweight	426	2.18% (1.92–2.44)	9.15% (6.17–12.12)	8.06% (4.84–11.28)	0.58% (0–4.59)
	Normal	5169	30.91% (29.9–31.91)	17.57% (16.13–19)	17.18% (15.62–18.72)	11.95% (10–13.88)
	Overweight	6447	39.73% (38.65–40.81)	21.43% (20.01–22.85)	21.35% (19.82–22.89)	17.16% (15.24–19.07)
	Obesity	4495	27.18% (26.2–28.15)	24.24% (22.55–25.93)	24.39% (22.56–26.22)	20.95% (18.67–23.23)
Covid-19 safety protocols
Mask		15192	94.39% (93.95–94.84)	20.73% (19.84–21.63)	20.6% (19.63–21.57)	16.21% (15.01–17.43)
Gloves		5522	36.01% (34.92–37.1)	21.12% (19.56–22.69)	21.02% (19.33–22.72)	16.74% (14.63–18.86)
Face shield		621	5.77% (5.12–6.42)	13.97% (10.68–17.25)	13.28% (9.72–16.83)	7.09% (2.65–11.51)
Washing hands		14732	90.96% (90.32–91.61)	20.81% (19.9–21.71)	20.68% (19.7–21.66)	16.32% (15.09–17.54)
Using public transport		4212	25.15% (24.21–26.09)	21.75% (20.11–23.4)	21.7% (19.92–23.48)	17.59% (15.38–19.82)
Having children attending school		1925	9.43% (8.89–9.97)	19.85% (17.55–22.15)	19.64% (17.15–22.13)	15.02% (11.92–18.13)
Number of daily contacts
	under 10	11988	73.82% (72.84–74.8)	21.02% (20–22.04)	20.91% (19.81–22.01)	16.6% (15.23–17.98)
	10 to 30	2884	17.9% (17.04–18.77)	20.07% (18.11–22.03)	19.88% (17.76–22)	15.32% (12.68–17.97)
	30 to 50	691	4.27% (3.84–4.7)	18.25% (14.63–21.87)	17.91% (13.99–21.83)	12.86% (7.98–17.75)
	50 and more	648	4.01% (3.59–4.42)	23.31% (18.99–27.63)	23.39% (18.71–28.06)	19.69% (13.86–25.53)

Table 3

The OR of the study variables and IgG seropositivity, crude and adjusted by age, sex, wealth index, and years of education
Variable	Status	Crude OR	Crude P value	Adjusted OR	Adjusted P value
Socio-demographic
Age (reference: 25–29)	30–39	1.096(0.883,1.361)	0.404	1.012(0.811,1.263)	0.917
	40–49	1.414(1.142,1.75)	0.002	1.261(1.006,1.58)	0.044
	50–59	1.747(1.412,2.162)	< 0.001	1.5(1.192,1.887)	0.001
	60 and above	1.951(1.58,2.408)	< 0.001	1.599(1.251,2.045)	< 0.001
Sex (reference: female)	Male	0.851(0.766,0.945)	0.003	0.867(0.775,0.969)	0.012
Education (reference: 0 years)	1 to 7 y	0.766(0.666,0.881)	< 0.001	0.772(0.659,0.904)	0.001
	7 to 12 y	0.638(0.544,0.748)	< 0.001	0.715(0.586,0.874)	0.001
	12 + y	0.552(0.477,0.639)	< 0.001	0.651(0.533,0.793)	< 0.001
Occupation (reference: unemployed)	Freelancer	1.091(0.851,1.398)	0.491	1.151(0.893,1.484)	0.279
	Private sector worker	0.884(0.637,1.228)	0.463	1.038(0.744,1.449)	0.825
	Public sector worker	1.015(0.748,1.377)	0.923	1.151(0.835,1.588)	0.391
	Retired	1.404(1.055,1.866)	0.02	1.182(0.872,1.604)	0.281
	Unpaid job	1.38(1.091,1.746)	0.007	1.279(0.955,1.714)	0.099
Marital status (reference: married)	Single	0.581(0.471,0.717)	< 0.001	0.747(0.593,0.942)	0.014
	Divorced/separate with partner	0.745(0.54,1.029)	0.074	0.837(0.602,1.165)	0.292
	Widow	1.331(1.099,1.613)	0.003	1.005(0.806,1.252)	0.967
Quality of life (reference: level 1)	2	0.93(0.802,1.079)	0.339	0.999(0.857,1.166)	0.992
	3	0.911(0.785,1.058)	0.222	1.018(0.872,1.188)	0.824
	4	0.786(0.663,0.931)	0.005	0.937(0.785,1.12)	0.477
	5	0.788(0.665,0.934)	0.006	0.928(0.777,1.108)	0.408
Wealth index (reference: low)	Low-middle	1.246(1.058,1.466)	0.008	1.348(1.144,1.588)	< 0.001
	Middle	1.231(1.055,1.436)	0.008	1.372(1.172,1.607)	< 0.001
	Upper-middle	1.292(1.099,1.518)	0.002	1.534(1.299,1.812)	< 0.001
	Richest	0.996(0.828,1.199)	0.97	1.228(1.01,1.493)	0.039
Comorbidities
Diabetes Mellitus		1.334(1.161,1.534)	< 0.001	1.104(0.949,1.283)	0.199
Hypertension		1.437(1.292,1.598)	< 0.001	1.143(1.004,1.3)	0.043
Hypercholesterolemia		1.372(1.229,1.532)	< 0.001	1.179(1.042,1.334)	0.009
MI (ever)		1.31(1.089,1.576)	0.004	1.047(0.856,1.281)	0.653
MI (last year)		1.389(0.969,1.993)	0.074	1.12(0.765,1.64)	0.559
Lung disease (ever)		1.239(1.006,1.526)	0.044	1.083(0.876,1.34)	0.461
Lung disease (last year)		1.256(0.953,1.653)	0.105	1.08(0.82,1.423)	0.584
Stroke (ever)		1.466(1.012,2.123)	0.043	1.231(0.832,1.821)	0.298
Stroke (last year)		1.199(0.546,2.634)	0.652	1.063(0.507,2.231)	0.872
Cancer (last year)		0.794(0.498,1.265)	0.332	0.74(0.453,1.208)	0.228
Kidney disease		1.388(1.195,1.612)	< 0.001	1.14(0.966,1.346)	0.12
Behavioral factors
Current cigarette smoker		0.521(0.412,0.659)	< 0.001	0.523(0.407,0.672)	< 0.001
Second-hand smoker		1.017(0.904,1.144)	0.775	1.045(0.92,1.187)	0.496
Physical activity (MET)		1.011(0.907,1.127)	0.845	0.944(0.841,1.059)	0.325
Toothbrush		0.843(0.749,0.95)	0.005	0.977(0.853,1.12)	0.739
Dental floss		0.927(0.805,1.068)	0.296	1.066(0.912,1.246)	0.42
No oral hygiene		1.174(1.035,1.33)	0.012	1.012(0.88,1.164)	0.865
Alcohol consumption frequency (reference: never)	Daily	0.215(0.027,1.721)	0.148	0.237(0.028,2.035)	0.19
	Weekly	0.629(0.323,1.224)	0.173	0.695(0.346,1.396)	0.306
	Monthly	0.802(0.405,1.59)	0.529	1.031(0.498,2.138)	0.934
	Seasonal	0.562(0.283,1.117)	0.1	0.654(0.321,1.331)	0.241
	6 months or more	1.222(0.773,1.932)	0.39	1.53(0.957,2.447)	0.076
BMI (reference: normal)	Underweight	0.472(0.326,0.685)	< 0.001	0.511(0.35,0.746)	0.001
BMI	Overweight	1.28(1.124,1.458)	< 0.001	1.25(1.09,1.434)	0.001
BMI	Obesity	1.502(1.311,1.719)	< 0.001	1.346(1.166,1.555)	< 0.001
COVID-19 safety protocols
Mask		0.908(0.738,1.117)	0.362	0.89(0.718,1.103)	0.288
Gloves		1.028(0.919,1.151)	0.626	1.028(0.916,1.154)	0.638
Face shield		0.602(0.456,0.795)	< 0.001	0.687(0.514,0.917)	0.011
Washing hands		0.987(0.818,1.192)	0.892	0.962(0.794,1.164)	0.689
Public transport		1.077(0.96,1.208)	0.206	1.055(0.932,1.195)	0.394
Children attending school		0.936(0.801,1.092)	0.4	0.984(0.83,1.165)	0.849
Number of daily contacts (reference: under 10)	10 to 30	0.943(0.823,1.082)	0.404	1.067(0.921,1.236)	0.388
	30 to 50	0.839(0.653,1.077)	0.168	1.031(0.791,1.345)	0.819
	50 and more	1.142(0.89,1.465)	0.297	1.447(1.11,1.887)	0.006

3.3 IgG seroprevalence association with comorbidities

Among the comorbidities including diabetes mellitus, hypertension, hypercholesterolemia, myocardial infarction, stroke, lung, kidney disease, and cancer history, in comparison with the reference groups (i.g., without diabetes mellitus) hypertension and hypercholesterolemia had the lowest P values of ORs. Accordingly, the IgG seroprevalence among participants with hypercholesterolemia was 21.26% (19.02–23.5) by the second adjustment (OR: 1.182 [1.044–1.339], P = 0.008); and the seroprevalence among participants with hypertension was 21.53% (19.47–23.6) by second adjustment (OR: 1.143 [1.004–1.3], P = 0.043) (Tables 2 and 3, Fig. 4).

3.4 IgG seroprevalence association with behavioral factors and BMI

Among the behavioral risk factors, including current cigarette smoking, frequency of alcohol consumption, physical activity, and oral hygiene, the only factor with significant effect on seroprevalence was current cigarette smoking (OR: 0.528 [0.41–0.679], P < 0.001).

Regarding the BMI, the underweight participants had lower, and overweight and obese participants had higher IgG seroprevalence than normal-BMI participants. The IgG seroprevalence among underweight participants was 0.58% (0–4.59) by the second adjustment (OR: 0.505 [0.344–0.741], P < 0.001) and The IgG seroprevalence among overweight and obese participants were 17.16% (15.24–19.07) and 20.95% (18.67–23.23) by the second adjustment, respectively (overweight OR: 1.234 [1.075–1.418], P = 0.003, obese OR: 1.333 [1.153–1.542], P < 0.001) (Tables 2 and 3, Fig. 4).

3.5 IgG seroprevalence association with COVID-19 safety protocols compliance

COVID-19 safety protocols studied here were wearing face masks, shields, gloves, washing hands, and the number of daily contacts. Face shields and the number of daily contacts were the factors having lowest P values. The seroprevalence of IgG among participants wearing face shields was 7.09% (2.65–11.51) by the second adjustment (OR: 0.692 [0.517–0.925], P = 0.013). The IgG seroprevalence among participants with the highest number of daily contacts (level four) was 19.69% (13.86–25.53) by the second adjustment (OR: 1.44 [1.102–1.882], P = 0.008) (Tables 2 and 3, Fig. 4).

The seroprevalence of IgG against SARS-CoV-2 in Iran was 20.63% and 16.25% at the time of study based on the first and second adjustments. At the province level, the age-standardized seroprevalence was relatively high among Kurdistan, West Azarbayejan, and Ilam, all of which were outskirt provinces on the west; possibly due to higher connections with neighboring countries. According to our results, male gender, smoking, higher education years, being underweight and being single were protective factors in COVID-19 infection.

Even in the second adjustment, the number of affected people by COVID-19 in Iran would approximately be 13,650,000, which is more than five times greater than the PCR-based registry report at the time of the study (1). However, it is worth re-mentioning that individuals under 25 years old were not included in this study. At that time, no one had received any vaccines. Hence, adjusted IgG positive results equal COVID-19 infection history. However, this number would even be higher if the IgG levels remained high among everyone after infection, which it does not (8, 13). Based on previous study estimates, up to the end of March 2021, the seroprevalence in Iran was 16.95 (12.91–21.01), which was higher than the global estimates and also greater than the seroprevalence in the western Asia region (14). Our study’s findings are consistent with these estimates based on the second adjustment. The reported seroprevalence here in Iran was higher than in the United States, Brazil, and China, and it was lower than in India, Russia, and South Africa (14).

Regarding the sociodemographic factors, in the present study, the male gender was a protective factor regarding the COVID-19 infection. However, males had higher seroprevalence in most countries worldwide due to higher daily interactions (14, 15). Nevertheless, there were some countries where there were no differences between males and females seropositivity rates (16). Although the significance of the difference remains discussable, a previous study by Poustchi et al. reported lower seroprevalence among males in Iran (11). Possible etiologies explaining the male gender being a protective factor needs to be investigated in future studies. Furthermore, in accordance with previous studies, the seroprevalence increases with age (17). However, ages older than 65 appeared to be less susceptible to SARS-CoV-2 infection due to lower social interactions in those studies. The results of this study do not support this explanation, probably because of high familial loyalty bonds in the culture of Iranian people preventing the elderly from being isolated. Higher wealth index was a risk, and higher education years were protective for SARS-CoV-2 seropositivity. In accordance, higher wealth status had a positive correlation with COVID-19 infection at the global level (18). One explanation supporting this finding might be the lower social network and person-to-person interactions among economically disadvantaged people (19). Expectedly, higher education years was negatively associated with COVID-19 seropositivity that might be reflecting higher preventive practices among educated people (20). Plus, being single was another protective factor for COVID-19 infection as in accordance with another study (20). Indeed, single people are more isolated and are not exposed to the risk of transmission through a higher number of household members (21, 22).

Regarding the comorbidities, hypertension and hypercholesterolemia were positively associated with IgG seropositivity among past medical history items. Both can be a risk of seropositivity due to their association with high BMI. On the other hand, hypertension and hypercholesterolemia are risks factors for developing more severe illnesses (23, 24). Acknowledging that the higher severity of the disease is associated with higher antibody response (25), it might be more likely to detect seropositive samples among people with hypertension and hypercholesterolemia.

Regarding the behavioral risk factors, smoking cigarettes was one of the protective factors in this study. This finding was consistent with previous studies founding smoking as a protective factor for COVID-19 infection (26, 27). This finding is not solely supported by observational studies. Indeed, the protective effect of smoking for COVID-19 infection has also been supported by a cohort study in England (28). However, further studies are required to come to more precise deductions regarding smoking (29). In case of BMI, underweight people were at lower while overweight and obese people were at higher risk of COVID-19 infection than normal-weight people. Higher BMI was also considered as risk factor for COVID-19 infection in the previous studies (20, 26, 30). Malnutrition and low hygiene accounted for higher infection risk among underweight people, but this just is mostly about low-income countries and in Iran, it does not appear to be true (31).

Eventually, all of these findings can be evaluated through the duration of the “remaining seropositive” point of view. Thus, we also analyzed the ORs between all variables with the COVID-19 infection claims (Table S2). Most of the findings of this analysis supported the association with IgG seropositivity, which could also be demonstrating the extent of validity of self-reports in this study.

Finally, among the COVID-19 safety protocols compliance variables, the high number of daily contacts was positively associated with COVID-19 infection, an expected result. In contrast, protocols such as wearing masks and washing hands did not have any associations. This paradox might be explained by different definitions of participants wearing masks or washing hands or people’s different attitudes towards adherence to wearing masks (32). For example, they might be wearing masks while driving alone but might also eat out at a crowded place not wearing one.

To the best of our knowledge, this is the first and the most comprehensive study determining the seroprevalence of IgG against SARS-CoV-2 in Iran through a nationwide and population-based study (11, 33–36). Moreover, the associations between sociodemographic variables, behaviors, past medical history, and compliance to COVID-19 safety protocols with the IgG seropositivity have been investigated using the STEPS 2021 survey in Iran. Using the STEPS to study seroprevalence has many other advantages. First, the data in the STEPS survey is weighted by sociodemographic. Hence, biased standard errors are avoided (37). Second, the high number of participants reduces the selection bias and makes the reported results more valid.

We also acknowledge the limitations of the study. Being cross-sectional and observational were two limitations of this study. Therefore, the reported associations could be due to several underlying factors that were not noticeable in this study, which might explain some of the controversial findings. Moreover, many people might have been affected by COVID-19, but their antibody titer was not high at the study time. This antibody titer duration might have confounded the results of this study. However, we have alternatively analyzed the associations with COVID-19 infection self-reports to reduce the effect of this limitation,

Based on this study, the COVID-19 infection rate in Iran was much more than the PCR-based registries reports at the time of study. Hence, it is evident that reported rates have been misleading. Furthermore, several factors can predict the risk of infection for individuals in the society. These factors range from the province of residence to marital status.

This population-based seroprevalence study provides insights into infection rates in Iran at the national and provincial levels. Therefore, it can be used as a landmark for future retrograde studies to determine the possible reasons for seroprevalence variabilities across the country. Such results would also be valuable for investigating the effectiveness of measures taken by governments and people in mitigating the infection rate for future waves of pandemics caused by resistant mutants. Besides, this study highlights the prominancy of seroprevalence studies in determining more realistic infection rates. In addition, identified associated factors with COVID-19 infection could help targeted rising awareness efforts and resource allocation.

Author contributions

MA-Kh, NR, and FF conceptualized the idea and designed the study. MA-Kh conducted the statistical analysis and drafted the manuscript. EG, NE, and NA re-analyzed the data and results and performed visualizations. M-MR, MA-Ka, JK, NR, and FF revised the manuscript critically. AK, AD-M, YF, MY, EA, MM, MN, and SD contributed to data recruitment and study settings. NF, SA, AA-D, and KR commented on the final draft.

Ethical approval

This study was approved by the Ethical Committee of the National Institute for Health Research under reference number IR.TUMS.NIHR.REC.1398.006.

Acknowledgment

The authors have no acknowledgments to declare

Funding

None. This study had no funding support and received no grants.

Data availability

The data used in this study is available upon reasonable request from the corresponding author.

Conflict of interests

The authors have no conflict of interests to declare

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No competing interests reported.

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Seroprevalence of Immunoglobulin G against SARS-CoV-2 in Iran at National and Sub- National Levels: Population-based Sero-Epidemiological Data from STEPS Survey 2021

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Overview

2.2 Data source

2.3 Variables

2.4 Serum IgG measurement and seroprevalence adjustment

2.5 Statistical Analysis

3. Results

3.1 SARS-CoV-2 seroprevalence

3.2 IgG seroprevalence association with sociodemographic factors

3.3 IgG seroprevalence association with comorbidities

3.4 IgG seroprevalence association with behavioral factors and BMI

3.5 IgG seroprevalence association with COVID-19 safety protocols compliance

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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